Context aware user interface

ABSTRACT

A computer-implemented method according to one embodiment includes identifying one or more historical instances of device usage; determining historical contextual data for each of the one or more historical instances of the device usage; creating an event in association with the one or more historical instances of the device usage and the historical contextual data; training a classifier to identify the event by inputting the historical contextual data and an associated event identifier into a machine learning algorithm; identifying, using the trained classifier, a real-time occurrence of the event, utilizing real-time contextual data and the historical contextual data, where the trained classifier analyzes the real-time contextual data to identify the real-time occurrence of the event; and in response to identifying the real-time occurrence of the event, adjusting one or more aspects of a user interface of the device based on the one or more historical instances of the device usage.

BACKGROUND

The present invention relates to user interfaces, and more specifically,this invention relates to adjusting a user interface based on a contextof historical instances of device usage.

Computing devices are a popular tool in modern society. The userinterface (UI) of these computing devices is the bridge that connectsthe devices to humans. However, a growing abundance of applications tobe used with the devices makes a present interaction with devicestime-consuming and unintuitive. As a result, there is a need for UIsthat understand personal usage patterns and the context of usage inorder to make the interaction between users and the UI easier and moredirect.

SUMMARY

A computer-implemented method according to one embodiment includesidentifying one or more historical instances of device usage;determining historical contextual data for each of the one or morehistorical instances of the device usage; creating an event inassociation with the one or more historical instances of the deviceusage and the historical contextual data; training a classifier toidentify the event by inputting the historical contextual data and anassociated event identifier into a machine learning algorithm;identifying, using the trained classifier, a real-time occurrence of theevent, utilizing real-time contextual data and the historical contextualdata, where the trained classifier analyzes the real-time contextualdata to identify the real-time occurrence of the event; and in responseto identifying the real-time occurrence of the event, adjusting one ormore aspects of a user interface (UI) of the device based on the one ormore historical instances of the device usage.

According to another embodiment, a computer program product forimplementing a context aware user interface (UI) comprises a computerreadable storage medium having program instructions embodied therewith,wherein the computer readable storage medium is not a transitory signalper se, and where the program instructions are executable by a processorto cause the processor to perform a method including identifying one ormore historical instances of device usage, utilizing the processor;determining historical contextual data for each of the one or morehistorical instances of the device usage, utilizing the processor;creating an event in association with the one or more historicalinstances of the device usage and the historical contextual data,utilizing the processor; training a classifier to identify the event byinputting the historical contextual data and an associated eventidentifier into a machine learning algorithm, utilizing the processor;identifying, using the trained classifier, a real-time occurrence of theevent, utilizing the processor and real-time contextual data and thehistorical contextual data, where the trained classifier analyzes thereal-time contextual data to identify the real-time occurrence of theevent; and in response to identifying the real-time occurrence of theevent, adjusting, utilizing the processor, one or more aspects of a UIof the device based on the one or more historical instances of thedevice usage.

A system according to another embodiment includes a processor, and logicintegrated with the processor, executable by the processor, orintegrated with and executable by the processor, where the logic isconfigured to identify one or more historical instances of device usage;determine historical contextual data for each of the one or morehistorical instances of the device usage; create an event in associationwith the one or more historical instances of the device usage and thehistorical contextual data; train a classifier to identify the event byinputting the historical contextual data and an associated eventidentifier into a machine learning algorithm; identify, using thetrained classifier, a real-time occurrence of the event, utilizingreal-time contextual data and the historical contextual data, where thetrained classifier analyzes the real-time contextual data to identifythe real-time occurrence of the event; and in response to identifyingthe real-time occurrence of the event, adjust one or more aspects of auser interface (UI) of the device based on the one or more historicalinstances of the device usage.

Other aspects and embodiments of the present invention will becomeapparent from the following detailed description, which, when taken inconjunction with the drawings, illustrate by way of example theprinciples of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing node according to an embodiment of thepresent invention.

FIG. 2 depicts a cloud computing environment according to an embodimentof the present invention.

FIG. 3 depicts abstraction model layers according to an embodiment ofthe present invention.

FIG. 4 illustrates a method for implementing a context aware userinterface, in accordance with one embodiment.

FIG. 5 illustrates a method for obtaining historical data for training,in accordance with one embodiment.

FIG. 6 illustrates a method for configuring an interface in real-time,in accordance with one embodiment.

DETAILED DESCRIPTION

The following description discloses several preferred embodiments ofsystems, methods and computer program products for implementing acontext aware user interface. Various embodiments provide a method foridentifying historical usage of devices, as well as a context of thatusage, and using such historical usage and context to adjust aninterface of the devices.

The following description is made for the purpose of illustrating thegeneral principles of the present invention and is not meant to limitthe inventive concepts claimed herein. Further, particular featuresdescribed herein can be used in combination with other describedfeatures in each of the various possible combinations and permutations.

Unless otherwise specifically defined herein, all terms are to be giventheir broadest possible interpretation including meanings implied fromthe specification as well as meanings understood by those skilled in theart and/or as defined in dictionaries, treatises, etc.

It must also be noted that, as used in the specification and theappended claims, the singular forms “a,” “an” and “the” include pluralreferents unless otherwise specified. It will be further understood thatthe terms “includes” and/or “comprising,” when used in thisspecification, specify the presence of stated features, integers, steps,operations, elements, and/or components, but do not preclude thepresence or addition of one or more other features, integers, steps,operations, elements, components, and/or groups thereof.

The following description discloses several preferred embodiments ofsystems, methods and computer program products for implementing acontext aware user interface.

In one general embodiment, a computer-implemented method includesidentifying one or more historical instances of device usage,determining historical contextual data for each of the one or morehistorical instances of the device usage, creating an event inassociation with the one or more historical instances of the deviceusage and the historical contextual data, identifying a real-timeoccurrence of the event by comparing real-time contextual data to thehistorical contextual data, and in response to identifying the real-timeoccurrence of the event, adjusting one or more aspects of a userinterface (UI) of the device based on the one or more historicalinstances of the device usage.

In another general embodiment, a computer program product forimplementing a context aware user interface (UI) comprises a computerreadable storage medium having program instructions embodied therewith,wherein the computer readable storage medium is not a transitory signalper se, and where the program instructions are executable by a processorto cause the processor to perform a method comprising identifying one ormore historical instances of device usage, utilizing the processor,determining historical contextual data for each of the one or morehistorical instances of the device usage, utilizing the processor,creating an event in association with the one or more historicalinstances of the device usage and the historical contextual data,utilizing the processor, identifying, utilizing the processor, areal-time occurrence of the event by comparing real-time contextual datato the historical contextual data, and in response to identifying thereal-time occurrence of the event, adjusting, utilizing the processor,one or more aspects of a UI of the device based on the one or morehistorical instances of the device usage.

In another general embodiment, a system includes a processor, and logicintegrated with the processor, executable by the processor, orintegrated with and executable by the processor, where the logic isconfigured to identify one or more historical instances of device usage,determine historical contextual data for each of the one or morehistorical instances of the device usage, create an event in associationwith the one or more historical instances of the device usage and thehistorical contextual data, identify a real-time occurrence of the eventby comparing real-time contextual data to the historical contextualdata, and in response to identifying the real-time occurrence of theevent, adjust one or more aspects of a user interface (UI) of the devicebased on the one or more historical instances of the device usage.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 1, a schematic of an example of a cloud computingnode is shown. Cloud computing node 10 is only one example of a suitablecloud computing node and is not intended to suggest any limitation as tothe scope of use or functionality of embodiments of the inventiondescribed herein. Regardless, cloud computing node 10 is capable ofbeing implemented and/or performing any of the functionality set forthhereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, hand-held or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

Referring now to FIG. 2, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 2 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 3, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 2) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 3 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and language and behavior determination 96.

Now referring to FIG. 4, a flowchart of a method 400 is shown accordingto one embodiment. The method 400 may be performed in accordance withthe present invention in any of the environments depicted in FIGS. 1-3,among others, in various embodiments. Of course, more or less operationsthan those specifically described in FIG. 4 may be included in method400, as would be understood by one of skill in the art upon reading thepresent descriptions.

Each of the steps of the method 400 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 400 may be partially or entirely performed byone or more servers, computers, or some other device having one or moreprocessors therein. The processor, e.g., processing circuit(s), chip(s),and/or module(s) implemented in hardware and/or software, and preferablyhaving at least one hardware component may be utilized in any device toperform one or more steps of the method 400. Illustrative processorsinclude, but are not limited to, a central processing unit (CPU), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), etc., combinations thereof, or any other suitablecomputing device known in the art.

As shown in FIG. 4, method 400 may initiate with operation 402, whereone or more historical instances of device usage are identified. In oneembodiment, each instance of historical instances of device usage mayinclude a past performance of one or more actions utilizing a device bya user. For example, the one or more actions may include running of oneor more applications by a device, sending and/or receiving data by thedevice, etc. In another embodiment, the device may include any computingdevice. For example, the device may include a mobile computing device, adesktop computing device, a wearable computing device, a virtual realitydevice, etc.

Additionally, in one embodiment, the one or more historical instances ofthe device usage may be identified for a single user. In anotherembodiment, the one or more historical instances of the device usage maybe identified for a plurality of users. For example, the one or morehistorical instances of the device usage may be identified for apredetermined group of users (e.g., users within a predeterminedorganization, etc.). In another embodiment, the one or more historicalinstances of the device usage may be identified by monitoring deviceusage, logging historical instances of device usage, etc.

In yet another embodiment, the one or more historical instances of thedevice usage may be received at a cloud computing environment. Forexample, the one or more historical instances of the device usage may besent from one or more devices (e.g., mobile computing devices, etc.) tothe cloud computing environment.

Further, as shown in FIG. 4, method 400 may proceed with operation 404,where historical contextual data is determined for each of the one ormore historical instances of the device usage. In one embodiment, thehistorical contextual data may include a time each instance of usageoccurred, a location where each instance of usage occurred, etc. Inanother embodiment, the historical contextual data may include anactivity being performed by a user during each instance of historicalinstances of device usage by the user. For example, the activity mayinclude running, driving, sitting, etc.

Further still, in one embodiment, the historical contextual data mayinclude a role of a user performing the historical instances of deviceusage. For example, the role may include that of a customer during atransaction, a presented during a meeting, etc. In another embodiment,the historical contextual data may include a proximity and relevance ofone or more devices in communication with the device being used. Forexample, the historical contextual data may consider the internet ofthings (IoT) and may include an identification of one or more devices(e.g., point of sale sensors, computing devices, beacons, etc.) thatcommunicated with the device (or were within communication range of thedevice) during the historical usage of the device.

Also, in one embodiment, the historical contextual data may include anidentification of external data associated with the one or morehistorical instances of the device usage. For example, the external datamay include calendar data (e.g., data stored within a calendarapplication indicating a scheduled event, etc.), email data (e.g., datastored within a messaging application indicating a scheduled event,etc.), etc.

In another embodiment, the historical contextual data may be received ata cloud computing environment. For example, the historical contextualdata may be sent from one or more devices (e.g., mobile computingdevices, etc.) to the cloud computing environment.

In addition, as shown in FIG. 4, method 400 may proceed with operation406, where an event is created in association with the one or morehistorical instances of the device usage and the historical contextualdata. In one embodiment, creating the event may include giving the eventan event identifier. In another embodiment, the creating the event mayinclude associating the event identifier with the one or more historicalinstances of the device usage as well as the historical contextual data.For example, the one or more historical instances of the device usageand the historical contextual data may all be labelled with the eventidentifier.

Furthermore, in one embodiment, the event identifier may include adescription of the event. For example, the event identifier may includeterms such as “meeting,” “purchasing lunch,” “working at desk,” etc. Inanother embodiment, the event may be manually created (e.g., by anadministrator, by the user, etc.). In another embodiment, the event maybe automatically determined (e.g., based on past event creation inassociation with other historical instances of device usage for one ormore users, etc.). In yet another embodiment, the event may be createdwithin a cloud computing environment.

Further still, as shown in FIG. 4, method 400 may proceed with operation408, where a real-time occurrence of the event is identified bycomparing real-time contextual data to the historical contextual data.In one embodiment, the real-time contextual data may include contextualdata identified by monitoring one or more aspects of a user's real-timedevice usage. For example, the real-time contextual data may beidentified by monitoring a location of a user, a time of device usage bythe user, calendar data created by or for the user, etc.

Also, in one embodiment, the occurrence of the event may be identifiedby comparing the real-time contextual data to historical contextual datathat is labelled with the event identifier for the event. In anotherembodiment, when all (or a portion) of the real-time data matches thehistorical contextual data labelled with the event identifier for theevent, an occurrence of the event may be identified. In yet anotherembodiment, when an amount of the real-time data matching the historicalcontextual data labelled with the event identifier for the event exceedsa predetermined threshold, an occurrence of the event may be identified.

Additionally, in one embodiment, a classifier may be trained to identifythe occurrence of the event. For example, the classifier may be trainedwith the historical contextual data and associated event identifier. Theclassifier may then analyze input real-time contextual data in order toidentify the occurrence of the event in response to detecting dataassociated with the event identifier.

In another embodiment, the occurrence of the event may be identifiedutilizing a server computer and/or a cloud computing environment. Forexample, the historical contextual data may be stored at a cloudcomputing environment and may be compared to real-time contextual datareceived at the cloud computing environment.

Further, as shown in FIG. 4, method 400 may proceed with operation 410,where in response to identifying the occurrence of the event, one ormore aspects of a user interface (UI) of the device are adjusted basedon the one or more historical instances of the device usage. In oneembodiment, the one or more aspects of the UI of the device may beadjusted based on all historical instances of device usage associatedwith the event (e.g., labelled with the event identifier, etc.). Forexample, the historical instances of device usage may be analyzed todetermine one or more applications that are likely to be used by theuser of the device during the occurrence of the event.

Further still, in one embodiment, an application selector may be trainedto identify one or more applications likely to be used during theoccurrence of the event. For example, the application selector may betrained with details of historical instances of device usage associatedwith the event identifier, and may then identify one or more relevantapplications in response to the identification of the occurrence of theevent.

Also, in one embodiment, adjusting the one or more aspects of the UI ofthe device may include selecting one or more applications to be run onthe one or more devices. In another embodiment, adjusting the one ormore aspects of the UI of the device may include configuring the UI ofthe device. For example, adjusting the one or more aspects of the UI ofthe device may include changing an order of one or more applicationsdisplayed using the UI, changing a size of one or more icons displayedby the UI, changing a placement of icons displayed by the UI, changing acolor of one or more icons displayed by the UI, changing an opacity ofone or more icons displayed using the UI, etc.

In addition, in one embodiment, one or more additional devices that arecurrently available to the user may be determined in response toidentifying the occurrence of the event. For example, all devices incommunication with a first user device may be determined (e.g.,utilizing one or more wireless communication protocols, a globalpositioning system (GPS), one or more beacons, etc.). In anotherembodiment, one or more aspects of the UI of the one or additionaldevices may be adjusted in response to identifying the occurrence of theevent.

In this way, historical usage and context may be analyzed in order totailor a UI of a device to certain situations in real-time.

Now referring to FIG. 5, a flowchart of a method 500 for obtaininghistorical data for training is shown according to one embodiment. Themethod 500 may be performed in accordance with the present invention inany of the environments depicted in FIGS. 1-3, among others, in variousembodiments. Of course, more or less operations than those specificallydescribed in FIG. 5 may be included in method 500, as would beunderstood by one of skill in the art upon reading the presentdescriptions.

Each of the steps of the method 500 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 500 may be partially or entirely performed byone or more servers, computers, or some other device having one or moreprocessors therein. The processor, e.g., processing circuit(s), chip(s),and/or module(s) implemented in hardware and/or software, and preferablyhaving at least one hardware component may be utilized in any device toperform one or more steps of the method 500. Illustrative processorsinclude, but are not limited to, a central processing unit (CPU), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), etc., combinations thereof, or any other suitablecomputing device known in the art.

As shown in FIG. 5, method 500 may initiate with operation 502, wherehistorical application usage data of one or more devices is collected.In one embodiment, the application usage data may include datadescribing personal usage of a device. For example, the applicationusage data may indicate a use of an application to purchase an item,send an email message, draft a document, present material visually, etc.In another embodiment, the application usage data may include datadescribing application usage by a plurality of users within a group(e.g., an organization, a club, etc.). In yet another embodiment, theapplication usage data may include data describing application usage byone or more devices themselves. For example, a device may utilize one ormore applications to automatically access one or more networks, senddata, receive data, etc.

Additionally, method 500 may proceed with operation 504, wherehistorical contextual data associated with the historical applicationusage data is identified. In one embodiment, the historical contextualdata may include a location of application usage, a time of applicationusage, etc. In another embodiment, the historical contextual data may beidentified using software (e.g., clock software, etc.), globalpositioning system (GPS) hardware and software, hardware and softwarebeacons, etc.

Further, method 500 may proceed with operation 506, where historicalexternal data associated with the historical application usage data isidentified. In one embodiment, the historical external data may includecalendar data indicating an event, a planned usage of an application,etc. In another embodiment, the historical external data may includemessage data (e.g., email message data, text message data, etc.)indicating an event, a planned usage of an application, etc.

Further still, method 500 may proceed with operation 508, where thehistorical application usage data, the historical contextual data, andthe historical external data are associated with an event. For example,an event identifier (e.g., a label, etc.) may be added to the historicalapplication usage data, the historical contextual data, and thehistorical external data. In another embodiment, the event identifiermay be added manually by a user or may be added automatically based onpast labels and event identifications.

Also, method 500 may proceed with operation 510, where a classifier istrained to identify the event based on real-time contextual and externaldata, where the training is performed utilizing the historicalcontextual data and the historical external data. In one embodiment,training the classifier may include inputting the historical contextualdata, the historical external data, and the associated event identifierinto a supervised machine learning algorithm so that the classifier mayrecognize an occurrence of an event in response to detecting real-timecontextual and external data matching the historical contextual andexternal data.

In addition, method 500 may proceed with operation 512, where anapplication selector is trained to predict application usage data inresponse to real-time identification of the event. In one embodiment,the application selector may be trained by inputting the historicalapplication usage data and associated event identifier into a supervisedmachine learning algorithm so that the application selector may predictan application to be used by a particular device in response to arecognition of an occurrence of an event.

Now referring to FIG. 6, a flowchart of a method 600 for configuring aninterface in real-time is shown according to one embodiment. The method600 may be performed in accordance with the present invention in any ofthe environments depicted in FIGS. 1-3, among others, in variousembodiments. Of course, more or less operations than those specificallydescribed in FIG. 5 may be included in method 500, as would beunderstood by one of skill in the art upon reading the presentdescriptions.

Each of the steps of the method 600 may be performed by any suitablecomponent of the operating environment. For example, in variousembodiments, the method 600 may be partially or entirely performed byone or more servers, computers, or some other device having one or moreprocessors therein. The processor, e.g., processing circuit(s), chip(s),and/or module(s) implemented in hardware and/or software, and preferablyhaving at least one hardware component may be utilized in any device toperform one or more steps of the method 600. Illustrative processorsinclude, but are not limited to, a central processing unit (CPU), anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), etc., combinations thereof, or any other suitablecomputing device known in the art.

As shown in FIG. 6, method 600 may initiate with operation 602, where areal-time contextual data feed is received. In one embodiment, thereal-time contextual data feed may include contextual data received froma plurality of users in real-time. For example, the real-time contextualdata feed may include a current location of one or more users, a currenttime, etc. In another embodiment, the real-time contextual data feed mayinclude external data (e.g., current calendar information, currentmessage information, etc.).

Additionally, method 600 may proceed with operation 604, where anoccurrence of an event is identified by comparing the real-timecontextual data feed to historical contextual data and historicalexternal data labeled with an identifier of the event. In oneembodiment, the identification may be made by a classifier trainedutilizing historical contextual data, historical external data, andassociated event labels.

Further, method 600 may proceed with operation 606, where all connecteddevices associated with users involved in the event are detected. In oneembodiment, the connected devices may include all devices currentlyaccessible by the users during the event. In another embodiment, theconnected devices may be detected by identifying all devices currentlyon one or more predetermined networks, currently within a predeterminedarea associated with the event, etc.

Further still, method 600 may proceed with operation 608, whereapplication selection and user interface (UI) configuration is performedon one or more of the connected devices, based on historical instancesof device usage labeled with the identifier of the event. In oneembodiment, the application selection may be performed utilizing anapplication selector trained utilizing historical instances of deviceusage and associated event labels. For example, the application selectormay predict an application to be used by a particular device in responseto a recognition of an occurrence of the event, and may therefore runthe application on the particular device when the event is detected.

Also, in one embodiment, the user interface configuration may includeadjusting a size of one or more application icons within the UI of oneor more of the devices. In another embodiment, the user interfaceconfiguration may include adjusting a placement of one or moreapplication icons within the UI of one or more of the devices. In yetanother embodiment, the user interface configuration may includeadjusting a color of one or more application icons within the UI of oneor more of the devices, an opacity of one or more application iconswithin the UI of one or more of the devices, etc.

In one exemplary scenario, a user may purchase an item on-site at aretail store. In this scenario, the historical usage data may include ause of a payment application on a mobile device of the user to pay forone or more items purchased by the user. The associated contextual datain this scenario may include a time of the usage of the paymentapplication, which may be identified using a clock of the mobile deviceused to send payment.

Further, additional contextual data may include a location of the userwithin the store, which may be identified utilizing a GPS module withinthe mobile device (or a beacon installed at a payment location withinthe retail store that identifies a distance of the mobile device fromthe payment location). Further contextual data may include a presence ofa wireless payment node at the store that is communicating with themobile device of the user. External data may include a text message sentby the user indicating that they are planning on purchasing an item atthe retail store.

Also, the historical usage data, contextual data, and external data maythen be labeled as “retail payment” data for the user. The contextualdata, external data, and “retail payment” label may then be used totrain a classifier in a cloud computing environment to identify a“retail payment” event by identifying real-time contextual and externaldata matching the historical contextual and external data. Thehistorical usage data and “retail payment” label may then be used totrain an application selector in a cloud computing environment todetermine that the payment application is to be run on the mobile deviceof the user when the “retail payment” event is identified.

Additionally, real-time contextual and external data is then identifiedand analyzed. When the real-time contextual and external data shows thatthe mobile device of the user is within the store and near a wirelesspayment node at the store, the classifier may determine that the “retailpayment” event is occurring for the user. In response to thisdetermination, the application selector may then reorganize applicationicons within a UI of the mobile device such that the payment applicationis listed as the first icon, is enlarged compared to other icons, etc.

In another exemplary scenario, a first user may present a report to aplurality of other users within a group. In this scenario, thehistorical usage data may include a use of a presentation application bya device of the first user to present the report, and a use of a wordprocessing application by devices of the plurality of other users withinthe group in order to take notes on the presentation.

Further, the associated contextual data in this scenario may include atime of the usage of the presentation and word processing applications,which may be identified using a clock of the devices used to present thereport and take notes. Additional contextual data may include a locationof the users within a building, which may be identified utilizing a GPSmodule within one or more mobile devices of the users (or a locationbeacon installed within a meeting room). External data may includecalendar data retrieved from one or more of the users indicating that ameeting is to take place at the time the report is presented, emailsdiscussing the meeting, etc.

Also, the historical usage data, contextual data, and external data maythen be labeled as “work meeting” data for each of the plurality ofusers. The contextual data, external data, and “work meeting” label maythen be used to train a classifier in a cloud computing environment toidentify a “work meeting” event by identifying real-time contextual andexternal data matching the historical contextual and external data. Thehistorical usage data and “work meeting” label may then be used to trainan application selector in a cloud computing environment to determinethat certain applications are to be run on devices of users within thegroup when the “work meeting” event is identified.

Additionally, real-time contextual and external data is then identifiedand analyzed. When the real-time contextual and external data shows thatthe users within the group are within a meeting room at a predeterminedtime, the classifier may determine that the “work meeting” event isoccurring for the group of users. In response to this determination, theapplication selector may automatically reorganize application iconswithin a UI of the device such that the presentation application islisted as the first icon, is enlarged, etc. The application selector mayalso automatically reorganize application icons within a UI of thedevices of the plurality of other users within the group such that theword processing application is listed as the first icon, is enlarged,etc.

In this way, an interaction of one or more users with their devices maybe improved, based on historical behavioral and contextual data.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein includes anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which includes one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Moreover, a system according to various embodiments may include aprocessor and logic integrated with and/or executable by the processor,the logic being configured to perform one or more of the process stepsrecited herein. By integrated with, what is meant is that the processorhas logic embedded therewith as hardware logic, such as an applicationspecific integrated circuit (ASIC), a FPGA, etc. By executable by theprocessor, what is meant is that the logic is hardware logic; softwarelogic such as firmware, part of an operating system, part of anapplication program; etc., or some combination of hardware and softwarelogic that is accessible by the processor and configured to cause theprocessor to perform some functionality upon execution by the processor.Software logic may be stored on local and/or remote memory of any memorytype, as known in the art. Any processor known in the art may be used,such as a software processor module and/or a hardware processor such asan ASIC, a FPGA, a central processing unit (CPU), an integrated circuit(IC), a graphics processing unit (GPU), etc.

It will be clear that the various features of the foregoing systemsand/or methodologies may be combined in any way, creating a plurality ofcombinations from the descriptions presented above.

It will be further appreciated that embodiments of the present inventionmay be provided in the form of a service deployed on behalf of acustomer to offer service on demand.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

What is claimed is:
 1. A computer-implemented method, comprising:identifying one or more historical instances of device usage;determining historical contextual data for each of the one or morehistorical instances of the device usage, the historical contextual dataincluding an identification of one or more additional devices thatcommunicated with the device during each of the one or more historicalinstances of the device usage; creating an event in association with theone or more historical instances of the device usage and the historicalcontextual data; training a classifier to identify the event byinputting the historical contextual data and an associated eventidentifier into a machine learning algorithm; training an applicationselector to predict application usage data in response to identificationof the event, including inputting the historical contextual data and theassociated event identifier into another machine learning algorithm totrain the application selector to predict one or more applications to beused by the one or more additional devices that communicated with thedevice; identifying, using the trained classifier, a real-timeoccurrence of the event, utilizing real-time contextual data and thehistorical contextual data, where the trained classifier analyzes thereal-time contextual data to identify the real-time occurrence of theevent; and in response to identifying the real-time occurrence of theevent, determining one or more additional devices that are incommunication with the device, and adjusting one or more aspects of auser interface (UI) of the one or more additional devices.
 2. Thecomputer-implemented method of claim 1, wherein adjusting the one ormore aspects of the UI of the one or more additional devices includes:changing an order of one or more applications displayed using the UI,changing a size of one or more icons displayed by the UI, changing aplacement of icons displayed by the UI, changing a color of one or moreicons displayed by the UI, and changing an opacity of one or more iconsdisplayed using the UI.
 3. The computer-implemented method of claim 1,wherein the historical contextual data include a time each of the one ormore historical instances of the device usage occurred and a locationwhere each of the one or more historical instances of the device usageoccurred.
 4. The computer-implemented method of claim 1, wherein thehistorical contextual data includes an activity being performed by auser during each the one or more historical instances of the deviceusage.
 5. The computer-implemented method of claim 1, wherein thehistorical contextual data includes an identification of external dataassociated with the one or more historical instances of the deviceusage.
 6. The computer-implemented method of claim 1, wherein creatingthe event includes associating an event identifier of the event with theone or more historical instances of the device usage and the historicalcontextual data.
 7. The computer-implemented method of claim 1, whereinthe real-time contextual data includes contextual data identified bymonitoring one or more aspects of a user's real-time device usage. 8.The computer-implemented method of claim 1, wherein: the real-timecontextual data includes current calendar information and currentmessage information, the historical contextual data includes: anactivity being performed by a user during each of the one or morehistorical instances, and a role of a user performing each of the one ormore historical instances.
 9. The computer-implemented method of claim1, wherein adjusting the one or more aspects of the UI of the deviceincludes changing an order of one or more applications displayed usingthe UI.
 10. The computer-implemented method of claim 1, wherein thehistorical contextual data is stored at a cloud computing environmentand is compared to real-time contextual data received at the cloudcomputing environment.
 11. The computer-implemented method of claim 1,wherein the real-time contextual data includes current calendarinformation and current message information.
 12. Thecomputer-implemented method of claim 1, wherein the historicalcontextual data includes external data associated with the one or morehistorical instances of the device usage, including data stored within acalendar application and a messaging application.
 13. Thecomputer-implemented method of claim 1, wherein the historicalcontextual data includes: an activity being performed by a user duringeach of the one or more historical instances, and a role of a userperforming each of the one or more historical instances.
 14. Thecomputer-implemented method of claim 1, wherein the one or morehistorical instances of the device usage are identified for apredetermined group of users.
 15. The computer-implemented method ofclaim 1, wherein the one or more additional devices in communicationwith the device are detected by identifying all devices currently withina predetermined area associated with the event.
 16. Thecomputer-implemented method of claim 1, wherein the one or moreadditional devices in communication with the device are detected byidentifying all devices currently on one or more predetermined networks.17. A computer program product for implementing a context aware userinterface (UI), the computer program product comprising a computerreadable storage medium having program instructions embodied therewith,wherein the computer readable storage medium is not a transitory signalper se, the program instructions executable by a processor to cause theprocessor to perform a method comprising: identifying one or morehistorical instances of device usage, utilizing the processor;determining historical contextual data for each of the one or morehistorical instances of the device usage, utilizing the processor, thehistorical contextual data including an identification of one or moreadditional devices that communicated with the device during each of theone or more historical instances of the device usage; creating an eventin association with the one or more historical instances of the deviceusage and the historical contextual data, utilizing the processor;training a classifier to identify the event by inputting the historicalcontextual data and an associated event identifier into a machinelearning algorithm, utilizing the processor; training, utilizing theprocessor, an application selector to predict application usage data inresponse to identification of the event, including inputting thehistorical contextual data and the associated event identifier intoanother machine learning algorithm to train the application selector topredict one or more applications to be used by the one or moreadditional devices that communicated with the device; identifying, usingthe trained classifier, a real-time occurrence of the event, utilizingthe processor and real-time contextual data and the historicalcontextual data, where the trained classifier analyzes the real-timecontextual data to identify the real-time occurrence of the event; andin response to identifying the real-time occurrence of the event,determining, utilizing the processor, one or more additional devicesthat are in communication with the device, and adjusting, utilizing theprocessor, one or more aspects of a user interface (UI) of the one ormore additional devices.
 18. The computer program product of claim 17,wherein the one or more historical instances of the device usage areidentified for a single user or for a predetermined group of users. 19.A system, comprising: a processor; and logic integrated with theprocessor, executable by the processor, or integrated with andexecutable by the processor, the logic being configured to: identify oneor more historical instances of device usage; determine historicalcontextual data for each of the one or more historical instances of thedevice usage, the historical contextual data including an identificationof one or more additional devices that communicated with the deviceduring each of the one or more historical instances of the device usage;create an event in association with the one or more historical instancesof the device usage and the historical contextual data; train aclassifier to identify the event by inputting the historical contextualdata and an associated event identifier into a machine learningalgorithm; train an application selector to predict application usagedata in response to identification of the event, including inputting thehistorical contextual data and the associated event identifier intoanother machine learning algorithm to train the application selector topredict one or more applications to be used by the one or moreadditional devices that communicated with the device; identify, usingthe trained classifier, a real-time occurrence of the event, utilizingreal-time contextual data and the historical contextual data, where thetrained classifier analyzes the real-time contextual data to identifythe real-time occurrence of the event; and in response to identifyingthe real-time occurrence of the event, determine one or more additionaldevices that are in communication with the device, and adjust one ormore aspects of a user interface (UI) of the one or more additionaldevices.